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EnForeSys - an Advanced Patient Enrollment Forecaster with Monte Carlo Simulations
PhUSE 2016 Barcelona Ritika Yadav, Cytel
Swechhya Bista, Cytel www.cytel.com
Uncertain*esinenrollmentprocess
Inabilitytomeettarget
Costand*meoverrun
Uncertain*esinenrollmentprocess
Inabilitytomeettarget
Costand*meoverrun
Clinical Trials - Challenges
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• 𝟑𝟑% 𝒐𝒇 𝒑𝒉𝒂𝒔𝒆 𝑰𝑰𝑰 𝒄𝒍𝒊𝒏𝒊𝒄𝒂𝒍 𝒕𝒓𝒊𝒂𝒍𝒔 𝒇𝒂𝒊𝒍
[Li,GenandGray,Robert,“Performance-BasedSiteSelec;onReducesCostsandShortensEnrollmentTime”,2011]
• 𝟑𝟕% 𝒔𝒊𝒕𝒆𝒔 𝒎𝒊𝒔𝒔 𝒆𝒏𝒓𝒐𝒍𝒍𝒎𝒆𝒏𝒕 𝒕𝒂𝒓𝒈𝒆𝒕
• 𝟏𝟏% 𝒏𝒐𝒕 𝒆𝒗𝒆𝒏 𝒆𝒏𝒓𝒐𝒍𝒍 𝒔𝒊𝒏𝒈𝒍𝒆 𝒔𝒖𝒃𝒋𝒆𝒄𝒕 [ImpactReportTu.sCSDD,2013]
• 80% 𝒐𝒇 𝒕𝒓𝒊𝒂𝒍𝒔 𝒇𝒂𝒊𝒍 𝒕𝒐 𝒎𝒆𝒆𝒕 𝒕𝒊𝒎𝒆𝒍𝒊𝒏𝒆𝒔
[Hess,Jon,“Web-BasedPaCentRecruitment,”CuFngEdgeinformaCon,hKp://www.cuFngedgeinfo.com/process/?ref=122]
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Num
bero
fsub
jects
ElapsedTime(Months)
EnrollmentPlotEnrolledSubjects Target
Start*me EnrollmentDura*on
Planning Solutions
• Straight Line Projections
Assumptions:
• All sites get ready at the start of the trial
• No site fails to recruit
• Subjects arrive at a constant rate
• No pause for Interim Analysis
• Simulations • Considers uncertainties in real world scenarios
• Accrual rate is not uniform
• Involves complex computation
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• Enrollment Forecasting System
• Software to simulate clinical trial enrollment
• Uses Monte Carlo Simulations
• Computes trial duration using the site and recruitment information.
• Factors impacting clinical trial enrollment: • Number of countries /sites
• Site initiation time
• Screening rates
• Screen and site failure rates
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SetTargetNumberofSubjects
IdenEfyParEcipaEng
Sites
UnopenedSites
OpenedSites
FailedSitesAtleastOneSubjectEnrolled
ScreenFailed Randomized
Steps in Enrollment Process
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Screening
Case Study • Impova - drug aimed at alleviating the symptoms of seasonal
allergies.
• Efficacy established in phase II trials
• Undergo phase III trial - for safety and efficacy
• To be tested on 700 subjects for a trial beginning on 1st January, 2017
• Subjects enrolled in multiple sites across ten countries
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Inputs
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SiteInputs
Inputs
RecruitmentInputs
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Scenario 1 • Screen and site failure rates - not considered
OverallTrialDuraEon
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Scenario 1
90%probabilityofachievingthetargetrandomizaEonin22months
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Scenario 1
Median Mean 95%CILegends: TotalSites
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Scenario 1
SimulaEonsaccuratelycapturesnon-linearityandrandomnessinenrollmentdata
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Median Mean 95%CILegends: Target
Scenario 1
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Scenario 2 • Trial duration = 18 months
EsEmatedRamp-upfactor
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Scenario 3 • Site failure rate = 0.1 & Screen failure rate = 0.2
OverallTrialDuraEon
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Scenario 3 • Site failure ratio = 0.1 & Screen failure ratio = 0.2
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Median:Scenario1 Median:Scenario3Legends:
Other Features • Milestone Probability
• Compare Plans
• Interim Analysis
• Import Functionality
• Edit Plan
• User Friendly UI
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Summary • Major challenge - Planning
• Simulation - better prediction
• EnForeSys:
• Uses Monte Carlo Simulations
• Realistic forecasting technique
• Accounts for non-linearity and randomness
• Reduces study costs and timelines
• Maximizes chances of study success.
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References • EnForeSys User Manual
• Impact Report Tufts CSDD, 2013
• Li, Gen and Gray, Robert, “ Performance-Based Site Selection Reduces Costs and Shortens Enrollment Time”, 2011
• Hess, Jon, “ Web-Based Patient Recruitment,” Cutting Edge information, http://www.cuttingedgeinfo.com/process/?ref=122
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